If "the Employee is Part of the Product" Then Boosting Investment in Employee Engagement Can Pay Off

Even if a “leaner” approach to employee staffing might make sense for some firms, at some times, there also is an argument to be made that for “high touch” customer experience” businesses, investing more in workers can pay off. 

Better treatment, training, scheduling, and support raise employee commitment, which tends to improve service quality, product knowledge, and consistency. 

That perhaps matters most in businesses where the employee is part of the product, such as retail, hospitality, call centers and healthcare. 

Even seemingly less important contributors such as stable scheduling can produce results, some studies find.

Study	Sector / setting	Investment or change	Outcome
Harvard Business Review / global retailer study	Large retail chain, customer-facing department	Better employee experience mix: more tenure, more prior rotations, more skill, more full-time staffing	Revenue up more than 50% and profits up nearly as much when stores moved from bottom to top quartile apollotechnical
Same HBR study	Large retail chain	Illustrative increase of $12 per employee-hour to reach top-quartile employee experience	About $18 more profit per hour; roughly 150% ROI in the simplified example apollotechnical
Gap stable-scheduling experiment	Retail stores	More predictable, stable schedules for sales associates	Median sales +7% and labor productivity +5% news.uchicago
Aberdeen / employee engagement research summarized by N2Growth	Multi-industry customer-facing firms	Formal employee engagement programs	39% greater annual growth in revenue from new customers n2growth
Employee engagement and profitability research summarized by Enterprise Engagement	Media organizations	Higher employee satisfaction/engagement	Better customer satisfaction and improved financial performance; engaged firms saw customers use products more and were more profitable enterpriseengagement

The best-supported pattern is not “pay more and sales automatically rise,” but rather “invest more intelligently in the employee experience and customer experience often improves enough to more than offset the cost.”

In customer-experience businesses, spending more on employees can be a growth investment, especially when that spending improves retention, skill, scheduling stability, and commitment, says Apollo Technical.  

Research shows that when customer-facing employees become more experienced, stable, and engaged, customer experience improves and sales can rise materially.

Employee engagement refers to how connected workers feel to your business; their sense of belonging and purpose in their role; their alignment with your company values; and how appreciated they feel by colleagues and superiors, Apollo Technical says. 

One might argue this matters less in some industries or businesses. It arguably matters much more in situations with a “high touch” people-dealing-with-people character. 

There, a correlation between how employees feel and how they treat customers arguably matters. 

If employees are highly engaged, they are more likely to provide positive customer experiences and build strong relationships with customers

According to Gallup, there is a direct correlation between engagement at work and organizational outcomes.

source: Gallup

Gallup looked at 736 research studies across 347 organizations in 53 industries, with employees in 90 countries. In total, Gallup studied 183,806 business and work units that included 3,354,784 employees.

It calculated the business and work-unit-level relationship between employee engagement and performance outcomes, studying 11 outcomes (including some quantifiable accounting outcomes and harder-to-quantify attitudes:

• customer loyalty/engagement

• profitability

• productivity

• turnover

• safety incidents

• absenteeism

• shrinkage

• patient safety incidents

• quality (defects)

• wellbeing

• organizational citizenship.

Across companies, business or work units scoring in the top half on employee engagement more than double their odds of success compared with those in the bottom half, Gallup says.  

Those at the 99th percentile have nearly five times the success rate of those at the first percentile.

The median percent differences in outcomes between top-quartile and bottom-quartile outcomes seem significant.

• 10% in customer loyalty/engagement

• 23% in profitability

• 18% in productivity (sales)

• 14% in productivity (production records and evaluations)

• 21% in turnover for high-turnover organizations (those with more than 40% annualized turnover)

• 51% in turnover for low-turnover organizations (those with 40% or lower annualized turnover)

• 63% in safety incidents (accidents)

• 78% in absenteeism

• 28% in shrinkage (theft)

• 58% in patient safety incidents (mortality and falls)

• 32% in quality (defects)

• 70% in wellbeing (thriving employees)

• 22% in organizational citizenship (participation)

The point might be that a focus on cost discipline often matters, but the the key is to evaluate labor as a value-creating input in service-heavy businesses, not just as a margin drag. 

The question becomes whether additional spending raises tenure, commitment, skill, and consistency enough to lift conversion, basket size, repeat visits, or retention. In the right business model, that tradeoff can produce a genuine turnaround rather than a cost overrun.

On the other hand, there is a meaningful body of evidence showing that employee satisfaction, by itself, does not reliably translate into better business outcomes.

Study	What was tested	Outcome on satisfaction link	Source
Gallup, “Employee Engagement vs. Employee Satisfaction and Customer Satisfaction”	Whether satisfaction itself predicts business performance better than engagement	Gallup argues satisfaction is often the wrong lever and that measured contentment alone frequently fails to improve business outcomes n2growth	Gallup article
Harter, Schmidt & Hayes (2002), summarized by Boise State	Business-unit employee satisfaction/engagement vs. productivity, profit, customer satisfaction, turnover, safety	Positive associations were found, but the summary stresses that these are correlations and not proof of causality n2growth	Boise State summary
Brown & Peterson, “The Effect of Effort on Sales Performance and Job Satisfaction”	Relationship between sales performance and job attitudes	Prior research cited in the paper “typically has found no empirical relationship” between performance and satisfaction journals.sagepub	SAGE journal record
Sales-management study summarized in Academia snippet	Satisfaction vs. performance among retail salespeople	The snippet reports that “performance is not related to satisfaction” academia	Academia record
Customer-contact satisfaction study summarized in search results	Salespeople’s work satisfaction vs. customer satisfaction	The relationship is described as being strongly moderated by customer and salesperson characteristics, meaning satisfaction alone was not enough academia	Search result summary

Gallup emphasizes that keeping employees “happy” is not the same as building engagement. 

Satisfaction may reflect how people feel, while outcomes depend on whether people actually change behavior in ways customers notice, one study suggests.  

A satisfied employee can still be poorly trained, misaligned with the job, or working in a process that prevents good service, so satisfaction alone may not move revenue, productivity, or retention. 

In other cases, a positive effect may exist only under specific conditions, such as high empathy, strong customer trust, or better sales management. 

So investing in employee engagement, in high touch businesses, might be viewed as a necessary, if not sufficient, step to produce better outcomes. 

Workers have to buy in, and reflect that commitment in service to customers.

OpenAI, Azure, Alphabet: Comparing Apples and Oranges

The adage about comparing apples and oranges is well illustrated by the many news reports suggesting the “AI trade” is alive and well after quarterly reports from Alphabet and Azure, which show robust cloud computing revenue growth. That is contrasted with the revenue issues OpenAI seems to be having. 

While all three are important contestants in the AI ecosystem, their business models, revenue drivers, and cost structures are fundamentally different.

Feature	Google Cloud / Azure	OpenAI
Role in Value Chain	Infrastructure & Compute (IaaS/PaaS)	AI Model & Application (SaaS)
Primary Driver	GPU/TPU rental and cloud storage	Model subscriptions and API usage
Exposure	Gains revenue from all AI players	Dependent on ChatGPT/Model dominance
Risk	High Capex, but diversified	High Burn, single-product dependency

The core of the disconnect lies in the distinction between selling the picks and shovels (infrastructure) and selling the gold (the end-user application). 

In other words, the value chain roles are different. Google Cloud and Azure sell infrastructure services (picks and shovels). Their revenue is driven by renting massive amounts of compute operations. 

OpenAI’s revenue is based on model operations. Success depends on software sales to end-users.

Also, Google and Microsoft are somewhat vertically integrated: infrastructure operations plus apps. 

The "AI Trade" for cloud providers is currently about scale. For OpenAI, the trade is about efficiency (profit margins).

The former is about industrial demand for compute services. The latter is about customer demand for a specific AI model. 

To be sure, if end user demand for model services breaks down, so will demand for AI compute services. But OpenAI’s issues seem company specific, essentially revolving around margin issues and growth rates, compared to the supporting investment in compute facilities. 

Azure, Google Cloud Revenue Growth Allays Some Fear about AI Infra Capex Levels

The latest batch of quarterly earnings reports from hyperscalers Amazon, Alphabet and Microsoft might not have put to rest concerns about high capital spending on artificial intelligence infrastructure by those firms, but earnings were still robust enough to reassure some about the wisdom of the investments. 

AWS sales were up 28 percent in the January-March period, the fastest increase in 15 quarters. 

Amazon Web Services had 24-percent sales growth in the fourth quarter, which followed the division's 20 percent growth in the third quarter of 2025. 

Microsoft cloud revenues were up 29 percent year-over-year, while Alphabet reported its cloud revenues grew 63 percent. 

source: Alphabet, Seeking Alpha 

Investors still seem to have qualms about Meta’s capex, though. 

Google Search: The Great Reversal

We might call the fortunes of Google search in the early artificial intelligence era as a “great reversal.” 

For much of two years, it seems, investors have asked a key question: what happens to Google search if users start asking ChatGPT, Perplexity, Gemini, or other AI assistants instead of typing queries into a traditional search bar?

“Shoot first, ask questions later” seems to have been an initial kneejerk reaction. 

But reality seemingly has moved in the opposite direction. 

In the first quarter of 2026, “Search and Other” revenue rose by 19.1 percent compared to the first quarter of 2025. Even more encouragingly, search growth accelerated for the fifth consecutive quarter.

For Alphabet, the debate is no longer only about whether AI will disrupt Search. It is now about whether Alphabet can use AI to protect Search, grow cloud offerings, increase monetization capabilities. 

source: Seeking Alpha 

This might alert us to the fact that our expectations about AI impact can be, not only wrong, but completely wrong.

Can a Good End be Produced by a Bad Means?

The U.S. Supreme Court has ruled, in a 6-3 decision, that Louisiana’s new congressional map, which includes districts based on race, is unconstitutional. The ruling is bound to be controversial. 

It might also be a bit nuanced. 

The majority found the map amounted to what it called unconstitutional “racial gerrymandering.” based on race, is a violation of the Equal Protection Clause of the Fourteenth Amendment. 

Critics will argue the decision effectively undercuts the 1965 Voting Rights Act, intended to end racial discrimination in voting practices, such as requiring literacy tests.

Supporters will argue the need for the law has long since been remedied. 

At least some will say the problem is the continuing use of “race” as a pillar of law, even if the intent of such efforts is to remedy past discrimination.

The issue at least some will have is that the solution to the problem of “racism” in law cannot be the enshrinement of racism in law, even if some believe it is done “for good reasons.”

Either before the VRA or since, if one continues to treat citizens differently because of their race, we haven’t really “solved” the problem of racism; we’ve only kept it in a new form. 

But the decision might not mean “race” cannot ever be a factor for voting rights: it simply cannot be the main motivation. 

Under the Constitution, states generally aren't allowed to use race as the primary tool for sorting voters unless they have an extremely good reason. 

Because race was the main factor, the map had to pass "strict scrutiny." The Court ruled the map failed because it wasn't "narrowly tailored.” It wasn't the most careful or necessary way to solve the legal issue.

Even if "allowing race to play any part in government decision-making represents a departure from the constitutional rule that applies in almost every other context," as Justice Samuel Alito wrote, the decision might not actually mean race can “never” be a consideration. 

But it moves policy in that direction. 

Separately, in June 2023, the Supreme Court effectively ended race-conscious affirmative action in college admissions, ruling in Students for Fair Admissions (SFFA) v. Harvard and SFFA v. UNC that such programs violate the 14th Amendment's Equal Protection Clause. 

That 6-3 ruling mandates that higher education admissions must use "colorblind" criteria, rejecting the use of race as a specific factor.

Opinion about both decisions will reveal a fundamental conflict over means and ends, as in the claim that “the ends justify the means” versus the argument that “the means are the ends.” In other words, can law and policy be “racist” in a new way to remedy the problem of racism?

Or, as the philosophical debate suggests, must the means match the desired ends?

Some argue the ends justify the means: the compelling end goal is to create a more equitable society by overcoming systemic racism. 

Others will argue an unethical or unconstitutional means is itself the problem. 

In other words, the debate is over ends and means. Does the goal of equality (the "end") justify discriminatory practices (the unequal "means").

To use a simple analogy, some might argue it is permissible to use hateful means to achieve a “loving end.” Others will argue that is impossible: the hateful means become the actual ends. 

Or, to put it simply, one cannot achieve a state of love using hate. In other words, must the means must embody the end they seek to create?

Mahatma Gandhi argued that means and ends are "two sides of the same coin". If the means are hateful, they "taint" the outcome, ensuring the final result is also characterized by hate or resentment.

Likewise, Martin Luther King Jr. noted that "hate cannot drive out hate; only love can do that". Using hateful means only intensifies the cycle of violence and adds "deeper darkness to a night already devoid of stars".

The "ends-means" debate splits between those who believe the government must use race-conscious means to reach a truly equitable end, and those who believe that using race as a means only perpetuates a discriminatory system.

AI Scarcities and Constraints Keep Evolving

It’s hard to keep up with the evolution of “value” in the artificial intelligence business as scarcities that create value keep shifting.

Between 2017 and early 2024, for example, scarcity and value in the AI value chain were heavily concentrated at the top of the stack:

• high-quality training data

• frontier model development(research talent, algorithms like transformers, and initial large-scale training runs). 

Compute, in the form of Nvidia graphics processing units,  was important, but a dominant bottleneck:

• inference was relatively cheap

• models were mostly accessed using APIs or research prototypes

• real-world deployment at scale was limited

• So value accrued to pioneers in data curation, model architecture, and cloud providers.

By 2026, constraints  have shifted with mass deployment:

• compute infrastructure (GPUs/accelerators, high-bandwidth memory/HBM, advanced packaging) remains scarce

• energy is emerging as a new scarcity (data center electricity, grid capacity, and permitting delays)

• physical infrastructure (data centers, land in power-rich locations, cooling) lags demand

• data scarcity is resurfacing as high-quality public data exhausts and regulations tighten

• model weights and foundational capabilities have commoditized somewhat

• supply chain crunches extend to materials like indium phosphide for optics and memory chips.

Overall, value has "inverted" toward the bottom of the stack, a shift from past decades where value accumulated in applications:

• infrastructure and physical hardware is scarce (chips, GPUs and accelerators, compute as a service, utilities, and energy firms)

• application-layer value (SaaS, agents, enterprise workflows) is growing but often depends on cheap/reliable inference, and therefore infrastructure

• consumer surplus from gen AI has risen sharply, but producer value capture is uneven.

Value Chain Role	Scarcity/Value ~2022–Early 2024	Scarcity/Value in 2026	Potential Future Scarcity/Value (2027+)
Data	High (internet-scale public data as fuel for scaling laws)	Rising (high-quality data "exhaustion"; regulations; shift to synthetic data)	High for specialized/real-time/enterprise/private data; synthetic data generation & curation
Models & Algorithms	Very High (frontier research, talent, architecture breakthroughs)	Moderate/Lowering (open-source closes gaps; commoditization of capable base models)	Lower for base models; High for specialized fine-tuning, agents, reasoning, or domain expertise
Training Compute	High (GPUs, clusters for large runs)	High but shifting (GPU/HBM shortages persist; diversification to custom ASICs)	Moderate (efficiency gains; more distributed/synthetic training)
Inference	Low (early, limited scale)	Very High (80-90% of lifetime costs; latency, memory, energy at scale; "factory" phase)	Extremely High (edge/on-device, long-context agents, real-time applications)
Infrastructure (Data Centers, Power, Cooling)	Moderate (cloud scaling)	Very High (energy/grid bottlenecks; power > chips as limiter; land/permitting)	Highest (energy access, nuclear/renewables integration, grid modernization)
Hardware Supply Chain	Moderate (Nvidia dominance emerging)	High (HBM, advanced packaging, optics, materials like indium phosphide)	High for specialized (inference-optimized, edge, robotics silicon)
Applications & Agents	Low (mostly prototypes)	Growing (enterprise adoption, workflows; value from integration)	High (autonomous agents, physical AI/robotics, real-world actions)
Physical World/Embodiment	Negligible	Emerging (early robotics interest)	Very High (humanoids, autonomous systems, sensors, actuators, real-world data loops)

Among the key shifts so far in 2026:

• value has moved downstream from "intelligence creation" (models/data) to "intelligence delivery and scaling" (inference and infrastructure)

• compute shortages have evolved into broader supply-chain and energy issues including

• power contracts

• tier-2 locations

• inference efficiency

• energy consumed per token.

Future scarcities could develop in the future: 

• embodied AI (robotics, sensors, actuators, energy storage, and unstructured environment handling)

• orchestration and decision-making (supply chains, logistics)

• regulatory compliance

• valuable applications that leverage abundance (as physical constraints lessen)

• geopolitics, materials, and talent for physical AI.

And, by definition, we don’t know what we don’t know. So we cannot predict what unknown issues might arise. 

"Known unknowns" in the AI value chain refer to recognized uncertainties or risks whose existence we acknowledge, even if we cannot precisely quantify their timing, magnitude, or full impact. 

These are issues we can model, debate, plan for, and partially mitigate through investment, policy, redundancy or research and development. 

In contrast, "unknown unknowns" are the true blind spots:

• risks

• emergent behaviors

• systemic shifts we do not yet realize exist. 

Unknown unknowns arise from emergent properties and non-linear interactions across the value chain:

• unpredictable model optimization for objectives not explicitly intended

• systemic supply chain compromises or cascading failures, such as AI agents acting as unpredictable "insider threats"

• transformative capability jumps or self-acceleration if AI begins automating large parts of its own R&D, training, or infrastructure design at unexpected speeds or in unforeseen directions

• disruption of labor markets, trust mechanisms, legal systems, or global power balances

• AI amplifying or interacting with unrelated disruptions or introducing fragility.

By definition, it is virtually impossible to plan for unknown unknowns, except to retain as much flexibility and adaptability as possible.